At Sage Bionetworks, we believe that we can learn more by learning from each other. By improving the way scientists collaborate, we help to make science more effective. We partner with researchers, patients, and healthcare innovators to drive collaborative data-driven science to improve health. Making science more open, collaborative, and inclusive ultimately advances biomedicine.
Are you the type of person who enjoys engaging with scientists and researchers across many disciplines? Do you have the ability to develop statistical and machine learning models to address computational problems in the area of mobile health? Do you want to be an integral part of a team that includes computational biologists, machine learning scientists, research ethicists, software engineers, and data librarians? If so, you could be our next Senior Research Scientist on the Mobile Health (mHealth) team. You would bring your expertise in data processing, statistical analysis, machine learning, and human health to extract meaningful insights from mobile and sensor data.
What you’ll be doing:
- Using quantitative analysis, signal processing, data mining and machine learning to analyse large amounts of sensor data from smartphones, wearables and other devices.
- Establishing the biological, clinical, and participant relevance of sensor data collected in digital health studies.
- Contributing to the improvement and automation of data processing and analysis pipelines across different mHealth studies.
- Designing and evaluating experiments to collect relevant digital endpoints.
- Building and analyzing dashboards and reports for external communications.
- Writing peer-reviewed journal articles to establish the biological and clinical significance of data collected in mHealth studies.
We’d love to hear from you if you:
- Are passionate about open science and collaboration.
- Are passionate about digital health technologies.
- Have an MS or PhD in a quantitative discipline (e.g., statistics, computational biology, biomedical engineering, computer science, applied mathematics, or similar), or another scientific degree with commensurate experience.
- Have at least an additional 2 years of experience in an analytical role developing both statistical and machine learning models.
- Are proficient at extracting, cleaning, and analyzing physiological or mobile sensor data sets using data/statistical tools such as R, Python, MATLAB, or similar.
- Have practical knowledge of digital signal processing and analysis of time-series data.
- Have expertise in exploratory and statistical data analysis (such as linear models, multivariate analysis, predictive modeling, and stochastic models).
- Demonstrate a track record of writing compelling publications in high impact, peer reviewed journals.
- Enjoy teaching others and learning new techniques.
- Are a strong communicator (oral and written).
- Familiarity with Deep Learning frameworks (Keras, TensorFlow, PyTorch etc.) is preferred.
- Experience with the analysis of sensor data from wearables or mobile sensors and clinical data is preferred
- Familiarity with software engineering practices and experience developing production software is preferred.
About Sage Bionetworks
Sage Bionetworks is a world-leading nonprofit biomedical research organization. We are dedicated to building and supporting open communities of collaborative research in human health and genomics. We are developing multiple initiatives designed to facilitate scientific collaborations and enable direct contributions of ideas and data from citizens to research projects.
Sage embraces diversity and equity. We offer a comprehensive benefits package, including relocation benefits, to bring the right talent to the team. We are based in Seattle, WA, and collaborate broadly throughout the world.